A Black Box Approach to EV Energy Consumption: Integrating Key Parameters for Drive Range Optimization

This paper aims to develop a black box model to improve the accuracy of drive range prediction in electric vehicles. Traditional models are typically based on limited parameters, often overlooking critical factors significantly impacting electric vehicles’ (EVs') performance and energy consumption. The paper aims to address the gaps in current electric vehicle energy usage models by developing a more comprehensive EV energy consumption model, known as the Black Box Model. The paper incorporates and integrates twenty-three diverse parameters into a single mathematical framework, including conventional factors and auxiliary variables, to positively influence drive range and enhance overall EV performance. The study classifies EVs into eight distinct energy consumption groups, identifying key parameters that lead to sub-optimal drive range predictions. These parameters are systematically incorporated into the model to improve the precision of energy consumption monitoring and drive range estimation. Using real-world data from Nissan Leaf SV Plus and the proposed model, an EV model is created in AVL eSuite for validation. The model’s results positively demonstrate the role of multi-parameter integration in energy consumption analysis and lay the groundwork for future advancements in optimizing EV drive range, contributing to the broader goal of sustainable and efficient electric mobility.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3591371
Citations
3
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
2.05
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article

A Black Box Approach to EV Energy Consumption: Integrating Key Parameters for Drive Range Optimization

Abdul Sattar Larik, Muhammad Arsalan Jalees Abro, Irfan Ahmed Halepoto, Ali Alqazzaz et al.
3 citations
IEEE Access
Vehicle emissions and performance
2.05
article

A Black Box Approach to EV Energy Consumption: Integrating Key Parameters for Drive Range Optimization

Abdul Sattar Larik, Muhammad Arsalan Jalees Abro, Irfan Ahmed Halepoto, Ali Alqazzaz, Samar M. Alqhtani, Mana Saleh Al Reshan, Asadullah Shaikh, Jotee Kumari Lohana
article en
3 citations

Abstract

This paper aims to develop a black box model to improve the accuracy of drive range prediction in electric vehicles. Traditional models are typically based on limited parameters, often overlooking critical factors significantly impacting electric vehicles’ (EVs') performance and energy consumption. The paper aims to address the gaps in current electric vehicle energy usage models by developing a more comprehensive EV energy consumption model, known as the Black Box Model. The paper incorporates and integrates twenty-three diverse parameters into a single mathematical framework, including conventional factors and auxiliary variables, to positively influence drive range and enhance overall EV performance. The study classifies EVs into eight distinct energy consumption groups, identifying key parameters that lead to sub-optimal drive range predictions. These parameters are systematically incorporated into the model to improve the precision of energy consumption monitoring and drive range estimation. Using real-world data from Nissan Leaf SV Plus and the proposed model, an EV model is created in AVL eSuite for validation. The model’s results positively demonstrate the role of multi-parameter integration in energy consumption analysis and lay the groundwork for future advancements in optimizing EV drive range, contributing to the broader goal of sustainable and efficient electric mobility.

IEEE AccessVol. 13
Mehran University of Engineering and Technology (PK), University of Bisha (SA), Najran University (SA), Aalborg University (DK)
Affordable and clean energy
Openalex Percentile: Top 13%
Vehicle emissions and performance
2.05
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